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Record W4410551260 · doi:10.1038/s41586-025-08997-x

Glioblastoma-instructed astrocytes suppress tumour-specific T cell immunity

2025· article· en· W4410551260 on OpenAlexaff
Camilo Faust Akl, Brian M. Andersen, Zhaorong Li, Federico Giovannoni, Martin Diebold, Liliana M. Sanmarco, Michael Kilian, Luca Fehrenbacher, Florian Pernin, Joseph M. Rone, Hong‐Gyun Lee, Gavin Piester, Jessica E. Kenison, Joon-Hyuk Lee, Tomer Illouz, Carolina Manganeli Polonio, Léna Srun, Jazmin Martínez, Elizabeth N Chung, Anton M Schüle, Agustín Plasencia, Lucinda Li, Kylynne Ferrara, Mercedes Lewandrowski, Craig A. Strathdee, Lorena Lerner, Christophe Quéva, Iain C. Clark, Benjamin Deneen, Judy Lieberman, David H. Sherr, Jack P. Antel, Michael A. Wheeler, Keith L. Ligon, E. Antonio Chiocca, Marco Prinz, David A. Reardon, Francisco J. Quintana

Bibliographic record

VenueNature · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMontreal Neurological Institute and Hospital
FundersNational Cancer InstituteNational Institute on Drug AbuseNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institute of Allergy and Infectious DiseasesNational Institute of Environmental Health SciencesU.S. Department of Veterans Affairs
KeywordsGlioblastomaImmunityNeuroscienceMedicineBiologyCancer researchImmunologyImmune system

Abstract

fetched live from OpenAlex

Abstract Introduction Glioblastoma (GBM) is an aggressive primary brain cancer with minimal response to current therapies. An immunosuppressive tumor microenvironment limits immunotherapeutic efficacy. Astrocytes are the most abundant glial cells in the central nervous system and play important roles in locally regulating immune responses. However, in the context of GBM little is known about mechanisms regulating astrocytes or their roles in the modulating anti-tumor immune responses. Methods We used single-cell and bulk RNA sequencing of human GBM specimens and murine preclinical models, multiplexed immunofluorescence, in vivo cell-specific genetic perturbations via CRISPR and in vitro mouse and human experimental astrocyte and GBM models to address this gap in knowledge. Results We identified an astrocyte subset which limits T cell anti-tumor responses by inducing T cell apoptosis via TRAIL, a death receptor ligand. Further, we identified IL-11 produced by GBM cells as a driver of STAT3 signaling in this astrocyte subset and a regulator of TRAIL expression. Astrocyte STAT3 signaling and TRAIL expression were correlated with more aggressive tumor progression and decreased survival in GBM patients. Following in vivo genetic inactivation of TRAIL or of the IL-11 receptor in astrocytes we observed extended survival in GBM mouse models, alongside enhanced T cell and macrophage responses. Finally, we engineered an oncolytic HSV-1 virus to express a TRAIL-blocking single-chain antibody in the tumor microenvironment. TRAIL blockade enhanced therapeutic effect of oncolytic viruses, extended survival and enhanced tumor-specific immunity in preclinical models of GBM. Conclusion In summary, we establish that IL-11—STAT3 signaling drives astrocytes to suppress GBM-specific immune responses by inducing TRAIL-dependent T cell apoptosis, and engineered a therapeutic strategy leveraging oncolytic viruses as delivery vectors to target this mechanism of astrocyte-driven immunosuppression in the tumor microenvironment. Funding Source This work was supported by grants NS102807, ES02530, ES029136, AI126880 from the NIH; RG4111A1 and JF2161-A-5 from the NMSS; RSG-14-198-01-LIB from the American Cancer Society; and PA-1604-08459 from the International Progressive MS Alliance. This work was further supported by a scholarship from the German Academic Exchange Service (DAAD). Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.243
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations47
Published2025
Admission routes1
Has abstractno

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